{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('3.2.1', '1.18.2', '1.0.3')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__, np.__version__, pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Plots side by side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "# sample data\n",
    "x = np.linspace(0.0,100,50)\n",
    "y = np.random.uniform(low=0,high=10,size=50)\n",
    "\n",
    "# create figure and axes\n",
    "fig, axes = plt.subplots(1,2)\n",
    "\n",
    "ax1 = axes[0]\n",
    "ax2 = axes[1]\n",
    "\n",
    "# just plot things on each individual axes\n",
    "ax1.scatter(x,y,c='red',marker='+')\n",
    "ax2.bar(x,y)\n",
    "\n",
    "plt.gcf().set_size_inches(10,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 plots one on top of the other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "# sample data\n",
    "x = np.linspace(0.0,100,50)\n",
    "y = np.random.uniform(low=0,high=10,size=50)\n",
    "\n",
    "# create figure and axes\n",
    "fig, axes = plt.subplots(2,1)\n",
    "\n",
    "ax1 = axes[0]\n",
    "ax2 = axes[1]\n",
    "\n",
    "# just plot things on each individual axes\n",
    "ax1.scatter(x,y,c='red',marker='+')\n",
    "ax2.bar(x,y)\n",
    "\n",
    "plt.gcf().set_size_inches(5,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 plots in a grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0.0,100,50)\n",
    "y = np.random.uniform(low=0,high=10,size=50)\n",
    "\n",
    "# plt.subplots returns an array of arrays. We can\n",
    "# directly assign those to variables directly\n",
    "# like this\n",
    "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n",
    "\n",
    "# just plot things on each individual axes\n",
    "ax1.scatter(x,y,c='red',marker='+')\n",
    "ax2.bar(x,y)\n",
    "ax3.scatter(x,y,marker='x')\n",
    "ax4.barh(x,y)\n",
    "\n",
    "plt.gcf().set_size_inches(5,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string_col</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baz</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>quux</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  string_col   x  y\n",
       "0        foo  10  1\n",
       "1        bar  20  2\n",
       "2        baz  30  3\n",
       "3       quux  40  4"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'string_col':['foo','bar','baz','quux'],\n",
    "    'x':[10,20,30,40],\n",
    "    'y':[1,2,3,4]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "# plt.subplots returns an array of arrays. We can\n",
    "# directly assign those to variables directly\n",
    "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n",
    "\n",
    "# bar plot for column 'x'\n",
    "df.plot(y='x', kind='bar', ax=ax1)\n",
    "ax1.set_xlabel('index')\n",
    "\n",
    "# horizontal bar plot for column 'y'\n",
    "df.plot(y='y', kind='bar', ax=ax2, color='orange')\n",
    "ax2.set_xlabel('index')\n",
    "\n",
    "# both columns in a scatter plot\n",
    "df.plot('x','y', kind='scatter', ax=ax3)\n",
    "\n",
    "# to have two lines, plot twice in the same axis\n",
    "df.plot(y='x', kind='line', ax=ax4)\n",
    "df.plot(y='y', kind='line', ax=ax4)\n",
    "ax4.set_xlabel('index')\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3, hspace=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set subplot title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "# plt.subplots returns an array of arrays. We can\n",
    "# directly assign those to variables directly\n",
    "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n",
    "\n",
    "# sample data\n",
    "x = np.linspace(0.0,100,50)\n",
    "y = np.random.uniform(low=0,high=10,size=50)\n",
    "\n",
    "# plot individual subplots\n",
    "ax1.bar(x,y)\n",
    "ax2.bar(x,y)\n",
    "ax3.scatter(x,y)\n",
    "ax4.plot(x)\n",
    "\n",
    "ax4.set_title('This is Plot 4',size=14)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3, hspace=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# sample data\n",
    "x = np.linspace(0.0,100,50)\n",
    "y = np.random.uniform(low=0,high=10,size=50)\n",
    "\n",
    "# plt.subplots returns an array of arrays. We can\n",
    "# directly assign those to variables directly\n",
    "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n",
    "\n",
    "# just plot things on each individual axes\n",
    "ax1.scatter(x,y,c='red',marker='+')\n",
    "ax2.bar(x,y)\n",
    "ax3.scatter(x,y,marker='x')\n",
    "ax4.barh(x,y)\n",
    "\n",
    "# here, set the width and the height between the subplots\n",
    "# the default value is 0.2 for each\n",
    "plt.subplots_adjust(wspace=0.50, hspace=1.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Align axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.clf()\n",
    "# plt.subplots returns an array of arrays. We can\n",
    "# directly assign those to variables directly\n",
    "fig, ((ax1,ax2)) = plt.subplots(1,2)\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "x = np.linspace(0.0,100,50)\n",
    "\n",
    "# sample data in different magnitudes\n",
    "y1 = np.random.normal(loc=10, scale=2, size=10)\n",
    "y2 = np.random.normal(loc=20, scale=2, size=10)\n",
    "\n",
    "ax1.plot(y1)\n",
    "ax2.plot(y2)\n",
    "\n",
    "ax1.grid(True,alpha=0.3)\n",
    "ax2.grid(True,alpha=0.3)\n",
    "\n",
    "ax1.set_ylim(0,25)\n",
    "ax2.set_ylim(0,25)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3, hspace=0.5)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
